def get_class_votes(self, X, ht):
# dist = {}
prediction_option = ht.leaf_prediction
# MC
if prediction_option == MAJORITY_CLASS:
dist = self.get_observed_class_distribution()
# NB
elif prediction_option == NAIVE_BAYES:
dist = do_naive_bayes_prediction(
X, self._observed_class_distribution,
self._attribute_observers)
# NBAdaptive
else:
if self._mc_correct_weight > self._nb_correct_weight:
dist = self.get_observed_class_distribution()
else:
dist = do_naive_bayes_prediction(
X, self._observed_class_distribution,
self._attribute_observers)
dist_sum = sum(dist.values()) # sum all values in dictionary
normalization_factor = dist_sum * self.get_error_estimation(
) * self.get_error_estimation()
if normalization_factor > 0.0:
normalize_values_in_dict(dist, normalization_factor)
return dist
def test_normalize_values_in_dict():
a_dictionary = {}
for k in range(10):
a_dictionary[k] = k * 10
reference = copy(a_dictionary)
sum_of_values = sum(a_dictionary.values())
normalize_values_in_dict(a_dictionary)
for k, v in a_dictionary.items():
assert a_dictionary[k] == reference[k] / sum_of_values
normalize_values_in_dict(a_dictionary, factor=1 / sum_of_values)
for k, v in a_dictionary.items():
assert a_dictionary[k] == reference[k]
def test_normalize_values_in_dict():
a_dictionary = {}
for k in range(1, 11):
a_dictionary[k] = k*10
reference = copy(a_dictionary)
sum_of_values = sum(a_dictionary.values())
normalize_values_in_dict(a_dictionary)
for k, v in a_dictionary.items():
assert a_dictionary[k] == reference[k] / sum_of_values
normalize_values_in_dict(a_dictionary, factor=1/sum_of_values)
for k, v in a_dictionary.items():
assert np.isclose(a_dictionary[k], reference[k])
b_dictionary = normalize_values_in_dict(a_dictionary, factor=1 / sum_of_values, inplace=False)
for k, v in a_dictionary.items():
assert a_dictionary[k] != b_dictionary[k]
assert id(a_dictionary) != id(b_dictionary)
def predict_proba(self, X):
r, _ = get_dimensions(X)
predictions = []
for i in range(r):
votes = self.get_votes_for_instance(X[i]).copy()
if votes == {}:
# Tree is empty, all classes equal, default to zero
predictions.append([0])
else:
new_votes = dict((key, d[key]) for d in votes for key in d)
if sum(new_votes.values()) != 0:
normalize_values_in_dict(new_votes)
if self.classes is not None:
y_proba = np.zeros(int(max(self.classes)) + 1)
else:
y_proba = np.zeros(int(max(new_votes.keys())) + 1)
for key, value in new_votes.items():
y_proba[int(key)] = value
predictions.append(y_proba)
return np.array(predictions)
def get_votes_for_instance(self, X):
if self.ensemble is None:
self.init_ensemble(X)
combined_votes = {}
for i in range(self.n_estimators):
vote = self.ensemble[i].get_votes_for_instance(X)
if vote != {} and sum(vote.values()) > 0:
normalize_values_in_dict(vote)
if not self.disable_weighted_vote:
performance = self.ensemble[i].evaluator.get_accuracy()\
if self.performance_metric == 'acc'\
else self.ensemble[i].evaluator.get_kappa()
if performance != 0.0: # CHECK How to handle negative (kappa) values?
for k in vote:
vote[k] = vote[k] * performance
# Add values
for k in vote:
try:
combined_votes[k] += vote[k]
except KeyError:
combined_votes[k] = vote[k]
return combined_votes
